Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

Abstract

To face the impact of climate change in all dimensions of our society in the near future, the European Union (EU) has established an ambitious target. Until 2050, the share of renewable power shall increase up to 75% of all power injected into nowadays’ power grids. While being clean and having become significantly cheaper, renewable energy sources (RES) still present an important disadvantage compared to conventional sources. They show strong fluctuations, which introduce significant uncertainties when predicting the global power outcome and confound the causes and mechanisms underlying the phenomena in the grid, such as blackouts, extreme events, and amplitude death. To properly understand the nature of these fluctuations and model them is one of the key challenges in future energy research worldwide. This review collects some of the most important and recent approaches to model and assess the behavior of power grids driven by renewable energy sources. The goal of this survey is to draw a map to facilitate the different stakeholders and power grid researchers to navigate through some of the most recent advances in this field. We present some of the main research questions underlying power grid functioning and monitoring, as well as the main modeling approaches. These models can be classified as AI- or mathematically inspired models and include dynamical systems, Bayesian inference, stochastic differential equations, machine learning methods, deep learning, reinforcement learning, and reservoir computing. The content is aimed at the broad audience potentially interested in this topic, including academic researchers, engineers, public policy, and decision-makers. Additionally, we also provide an overview of the main repositories and open sources of power grid data and related data sets, including wind speed measurements and other geophysical data.

Comprehensive Assessment and Comparative Analysis of Deep Learning Models for Large-Scale Renewable Energy Power Generation Prediction: A National Perspective

“…The use of these effective structures in solving classification and regression problems on energy systems is among the popular topics. In this regard, the article [2] provides a comprehensive exploration of challenges and advancements in modeling power grids driven by renewable energy sources, emphasizing the necessity to comprehend fluctuations in renewable energy. Various AI-and mathematically inspired models for power grid assessment are introduced, targeting a broad audience including researchers, engineers, policymakers, and decision-makers.…”

Modeling Wind-Speed Statistics beyond the Weibull Distribution

“…An accurate model for the distribution of wind-speed measurements is important in the present conjuncture of energy transfer strategies from conventional to renewable energy sources [8,9], since the dynamical features, including the high fluctuations and turbulent character of the wind speed, are reflected in the power output of wind turbines [10][11][12] and, consequently, in the economical viability of renewable energy strategies in many countries [13][14][15][16][17]. Models for the distributions of wind-speed measurements are fundamental to estimate the power output of wind turbines [18] and in the application of mathematical and artificial-intelligence methods to power-grid modeling and monitoring [19]. In particular, wind-speed fluctuations have been modeled with auto-regressive integrated moving average (ARIMA) models [20] to reproduce heavy-tail features of power output.…”

Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems

“…They are widely discussed in the source literature and usually provide good diagnostic results; however, they require a specific set of training data for each PV system unit. In the latest literature, you can also find the use of mathematical models for: the optimization of renewable energy systems taking into account several sources [37,38], business models facilitating decision-making in the field of PV microgrids [39], the modeling of photovoltaic cells as semiconductor materials [40][41][42] or maximum power point optimization (MPPT) [43,44]. The aforementioned numerous studies are aimed at developing methods for the remote, rapid and reliable diagnosis of actual photovoltaic micro-systems, which is economically justified.…”